Overview

Dataset statistics

Number of variables31
Number of observations561441
Missing cells2799718
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.8 MiB
Average record size in memory248.0 B

Variable types

Numeric20
Categorical5
Text6

Alerts

CANCELLED is highly imbalanced (87.5%)Imbalance
DIVERTED is highly imbalanced (97.2%)Imbalance
DEP_TIME has 9310 (1.7%) missing valuesMissing
DEP_DELAY has 9311 (1.7%) missing valuesMissing
TAXI_OUT has 9528 (1.7%) missing valuesMissing
TAXI_IN has 9769 (1.7%) missing valuesMissing
ARR_TIME has 9769 (1.7%) missing valuesMissing
ARR_DELAY has 11192 (2.0%) missing valuesMissing
CANCELLATION_CODE has 551852 (98.3%) missing valuesMissing
AIR_TIME has 11192 (2.0%) missing valuesMissing
CARRIER_DELAY has 435559 (77.6%) missing valuesMissing
WEATHER_DELAY has 435559 (77.6%) missing valuesMissing
NAS_DELAY has 435559 (77.6%) missing valuesMissing
SECURITY_DELAY has 435559 (77.6%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 435559 (77.6%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 22.39920382)Skewed
SECURITY_DELAY is highly skewed (γ1 = 50.38334166)Skewed
DEP_DELAY has 25076 (4.5%) zerosZeros
ARR_DELAY has 10477 (1.9%) zerosZeros
CARRIER_DELAY has 54172 (9.6%) zerosZeros
WEATHER_DELAY has 119465 (21.3%) zerosZeros
NAS_DELAY has 64125 (11.4%) zerosZeros
SECURITY_DELAY has 125147 (22.3%) zerosZeros
LATE_AIRCRAFT_DELAY has 59767 (10.6%) zerosZeros

Reproduction

Analysis started2024-03-30 06:15:45.166729
Analysis finished2024-03-30 06:20:12.391048
Duration4 minutes and 27.22 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1335136
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:12.498892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0356145
Coefficient of variation (CV)0.49246591
Kurtosis-1.2762366
Mean4.1335136
Median Absolute Deviation (MAD)2
Skewness-0.083392513
Sum2320724
Variance4.1437265
MonotonicityIncreasing
2024-03-30T03:20:12.821501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 94628
16.9%
6 84070
15.0%
5 78389
14.0%
4 78222
13.9%
1 78023
13.9%
3 74834
13.3%
2 73275
13.1%
ValueCountFrequency (%)
1 78023
13.9%
2 73275
13.1%
3 74834
13.3%
4 78222
13.9%
5 78389
14.0%
6 84070
15.0%
7 94628
16.9%
ValueCountFrequency (%)
7 94628
16.9%
6 84070
15.0%
5 78389
14.0%
4 78222
13.9%
3 74834
13.3%
2 73275
13.1%
1 78023
13.9%

FL_DATE
Categorical

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
4/14/2023 12:00:00 AM
 
19636
4/7/2023 12:00:00 AM
 
19591
4/21/2023 12:00:00 AM
 
19585
4/13/2023 12:00:00 AM
 
19585
4/28/2023 12:00:00 AM
 
19577
Other values (25)
463467 

Length

Max length21
Median length21
Mean length20.702419
Min length20

Characters and Unicode

Total characters11623187
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/3/2023 12:00:00 AM
2nd row4/3/2023 12:00:00 AM
3rd row4/3/2023 12:00:00 AM
4th row4/3/2023 12:00:00 AM
5th row4/3/2023 12:00:00 AM

Common Values

ValueCountFrequency (%)
4/14/2023 12:00:00 AM 19636
 
3.5%
4/7/2023 12:00:00 AM 19591
 
3.5%
4/21/2023 12:00:00 AM 19585
 
3.5%
4/13/2023 12:00:00 AM 19585
 
3.5%
4/28/2023 12:00:00 AM 19577
 
3.5%
4/6/2023 12:00:00 AM 19554
 
3.5%
4/20/2023 12:00:00 AM 19551
 
3.5%
4/27/2023 12:00:00 AM 19532
 
3.5%
4/10/2023 12:00:00 AM 19528
 
3.5%
4/24/2023 12:00:00 AM 19527
 
3.5%
Other values (20) 365775
65.1%

Length

2024-03-30T03:20:13.157513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 561441
33.3%
am 561441
33.3%
4/14/2023 19636
 
1.2%
4/7/2023 19591
 
1.2%
4/21/2023 19585
 
1.2%
4/13/2023 19585
 
1.2%
4/28/2023 19577
 
1.2%
4/6/2023 19554
 
1.2%
4/20/2023 19551
 
1.2%
4/27/2023 19532
 
1.2%
Other values (22) 404830
24.0%

Most occurring characters

ValueCountFrequency (%)
0 2865298
24.7%
2 1925775
16.6%
/ 1122882
 
9.7%
1122882
 
9.7%
: 1122882
 
9.7%
1 804740
 
6.9%
3 638513
 
5.5%
4 618835
 
5.3%
A 561441
 
4.8%
M 561441
 
4.8%
Other values (5) 278498
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11623187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2865298
24.7%
2 1925775
16.6%
/ 1122882
 
9.7%
1122882
 
9.7%
: 1122882
 
9.7%
1 804740
 
6.9%
3 638513
 
5.5%
4 618835
 
5.3%
A 561441
 
4.8%
M 561441
 
4.8%
Other values (5) 278498
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11623187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2865298
24.7%
2 1925775
16.6%
/ 1122882
 
9.7%
1122882
 
9.7%
: 1122882
 
9.7%
1 804740
 
6.9%
3 638513
 
5.5%
4 618835
 
5.3%
A 561441
 
4.8%
M 561441
 
4.8%
Other values (5) 278498
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11623187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2865298
24.7%
2 1925775
16.6%
/ 1122882
 
9.7%
1122882
 
9.7%
: 1122882
 
9.7%
1 804740
 
6.9%
3 638513
 
5.5%
4 618835
 
5.3%
A 561441
 
4.8%
M 561441
 
4.8%
Other values (5) 278498
 
2.4%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
WN
115946 
DL
80230 
AA
77039 
UA
57791 
OO
56048 
Other values (10)
174387 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1122882
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 115946
20.7%
DL 80230
14.3%
AA 77039
13.7%
UA 57791
10.3%
OO 56048
10.0%
YX 27104
 
4.8%
B6 24285
 
4.3%
NK 22751
 
4.1%
AS 19573
 
3.5%
MQ 18137
 
3.2%
Other values (5) 62537
11.1%

Length

2024-03-30T03:20:13.470002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 115946
20.7%
dl 80230
14.3%
aa 77039
13.7%
ua 57791
10.3%
oo 56048
10.0%
yx 27104
 
4.8%
b6 24285
 
4.3%
nk 22751
 
4.1%
as 19573
 
3.5%
mq 18137
 
3.2%
Other values (5) 62537
11.1%

Most occurring characters

ValueCountFrequency (%)
A 238110
21.2%
N 138697
12.4%
O 128129
11.4%
W 115946
10.3%
D 80230
 
7.1%
L 80230
 
7.1%
U 57791
 
5.1%
9 29344
 
2.6%
Y 27104
 
2.4%
X 27104
 
2.4%
Other values (11) 200197
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1122882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 238110
21.2%
N 138697
12.4%
O 128129
11.4%
W 115946
10.3%
D 80230
 
7.1%
L 80230
 
7.1%
U 57791
 
5.1%
9 29344
 
2.6%
Y 27104
 
2.4%
X 27104
 
2.4%
Other values (11) 200197
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1122882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 238110
21.2%
N 138697
12.4%
O 128129
11.4%
W 115946
10.3%
D 80230
 
7.1%
L 80230
 
7.1%
U 57791
 
5.1%
9 29344
 
2.6%
Y 27104
 
2.4%
X 27104
 
2.4%
Other values (11) 200197
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1122882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 238110
21.2%
N 138697
12.4%
O 128129
11.4%
W 115946
10.3%
D 80230
 
7.1%
L 80230
 
7.1%
U 57791
 
5.1%
9 29344
 
2.6%
Y 27104
 
2.4%
X 27104
 
2.4%
Other values (11) 200197
17.8%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5906
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2278.7027
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:13.826046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile278
Q11046
median2039
Q33312
95-th percentile5343
Maximum8819
Range8818
Interquartile range (IQR)2266

Descriptive statistics

Standard deviation1550.8626
Coefficient of variation (CV)0.68059016
Kurtosis-0.53211783
Mean2278.7027
Median Absolute Deviation (MAD)1067
Skewness0.62642928
Sum1.2793571 × 109
Variance2405174.8
MonotonicityNot monotonic
2024-03-30T03:20:14.267360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533 330
 
0.1%
777 307
 
0.1%
2086 306
 
0.1%
538 305
 
0.1%
2010 301
 
0.1%
360 284
 
0.1%
321 282
 
0.1%
1191 279
 
< 0.1%
374 273
 
< 0.1%
555 273
 
< 0.1%
Other values (5896) 558501
99.5%
ValueCountFrequency (%)
1 142
< 0.1%
2 179
< 0.1%
3 145
< 0.1%
4 176
< 0.1%
5 63
 
< 0.1%
6 70
 
< 0.1%
7 82
< 0.1%
8 71
 
< 0.1%
9 133
< 0.1%
10 201
< 0.1%
ValueCountFrequency (%)
8819 1
 
< 0.1%
8805 1
 
< 0.1%
8800 2
< 0.1%
8799 2
< 0.1%
8789 1
 
< 0.1%
8788 1
 
< 0.1%
8783 3
< 0.1%
8775 1
 
< 0.1%
8773 2
< 0.1%
8772 2
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12654.165
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:14.717505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1527.4141
Coefficient of variation (CV)0.12070446
Kurtosis-1.2973354
Mean12654.165
Median Absolute Deviation (MAD)1591
Skewness0.10124518
Sum7.104567 × 109
Variance2332994
MonotonicityNot monotonic
2024-03-30T03:20:15.181430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 27535
 
4.9%
11298 22870
 
4.1%
11292 22735
 
4.0%
13930 21185
 
3.8%
12889 15802
 
2.8%
11057 15674
 
2.8%
12892 15660
 
2.8%
14107 14928
 
2.7%
13204 14085
 
2.5%
12953 13816
 
2.5%
Other values (330) 377151
67.2%
ValueCountFrequency (%)
10135 350
 
0.1%
10136 88
 
< 0.1%
10140 1864
0.3%
10141 60
 
< 0.1%
10146 85
 
< 0.1%
10155 88
 
< 0.1%
10157 120
 
< 0.1%
10158 291
 
0.1%
10165 9
 
< 0.1%
10170 60
 
< 0.1%
ValueCountFrequency (%)
16869 139
 
< 0.1%
16218 120
 
< 0.1%
15991 60
 
< 0.1%
15919 910
0.2%
15841 60
 
< 0.1%
15624 793
0.1%
15607 87
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%
15412 1050
0.2%

ORIGIN
Text

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:16.005581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1684323
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROC
2nd rowITH
3rd rowJFK
4th rowCLE
5th rowJFK
ValueCountFrequency (%)
atl 27535
 
4.9%
dfw 22870
 
4.1%
den 22735
 
4.0%
ord 21185
 
3.8%
las 15802
 
2.8%
clt 15674
 
2.8%
lax 15660
 
2.8%
phx 14928
 
2.7%
mco 14085
 
2.5%
lga 13816
 
2.5%
Other values (330) 377151
67.2%
2024-03-30T03:20:17.207555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 190484
 
11.3%
L 155987
 
9.3%
S 144072
 
8.6%
D 130500
 
7.7%
T 88769
 
5.3%
O 86674
 
5.1%
C 84435
 
5.0%
M 75147
 
4.5%
F 70282
 
4.2%
P 66530
 
3.9%
Other values (16) 591443
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 190484
 
11.3%
L 155987
 
9.3%
S 144072
 
8.6%
D 130500
 
7.7%
T 88769
 
5.3%
O 86674
 
5.1%
C 84435
 
5.0%
M 75147
 
4.5%
F 70282
 
4.2%
P 66530
 
3.9%
Other values (16) 591443
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 190484
 
11.3%
L 155987
 
9.3%
S 144072
 
8.6%
D 130500
 
7.7%
T 88769
 
5.3%
O 86674
 
5.1%
C 84435
 
5.0%
M 75147
 
4.5%
F 70282
 
4.2%
P 66530
 
3.9%
Other values (16) 591443
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 190484
 
11.3%
L 155987
 
9.3%
S 144072
 
8.6%
D 130500
 
7.7%
T 88769
 
5.3%
O 86674
 
5.1%
C 84435
 
5.0%
M 75147
 
4.5%
F 70282
 
4.2%
P 66530
 
3.9%
Other values (16) 591443
35.1%
Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:17.935338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.06017
Min length8

Characters and Unicode

Total characters7332515
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRochester, NY
2nd rowIthaca/Cortland, NY
3rd rowNew York, NY
4th rowCleveland, OH
5th rowNew York, NY
ValueCountFrequency (%)
ca 59719
 
4.6%
tx 57175
 
4.4%
fl 53897
 
4.1%
ny 32235
 
2.5%
ga 29883
 
2.3%
new 29797
 
2.3%
il 29179
 
2.2%
san 28397
 
2.2%
chicago 28014
 
2.1%
atlanta 27535
 
2.1%
Other values (406) 935530
71.3%
2024-03-30T03:20:18.832688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
749920
 
10.2%
a 561828
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387093
 
5.3%
n 356877
 
4.9%
t 348529
 
4.8%
l 320764
 
4.4%
i 276083
 
3.8%
r 268802
 
3.7%
Other values (46) 3097698
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7332515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
749920
 
10.2%
a 561828
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387093
 
5.3%
n 356877
 
4.9%
t 348529
 
4.8%
l 320764
 
4.4%
i 276083
 
3.8%
r 268802
 
3.7%
Other values (46) 3097698
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7332515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
749920
 
10.2%
a 561828
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387093
 
5.3%
n 356877
 
4.9%
t 348529
 
4.8%
l 320764
 
4.4%
i 276083
 
3.8%
r 268802
 
3.7%
Other values (46) 3097698
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7332515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
749920
 
10.2%
a 561828
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387093
 
5.3%
n 356877
 
4.9%
t 348529
 
4.8%
l 320764
 
4.4%
i 276083
 
3.8%
r 268802
 
3.7%
Other values (46) 3097698
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:19.248185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1584049
Min length4

Characters and Unicode

Total characters4580463
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowOhio
5th rowNew York
ValueCountFrequency (%)
california 59719
 
9.3%
texas 57175
 
8.9%
florida 53897
 
8.4%
new 47455
 
7.4%
york 32235
 
5.0%
georgia 29883
 
4.6%
illinois 29179
 
4.5%
carolina 28055
 
4.3%
colorado 24955
 
3.9%
north 24262
 
3.8%
Other values (51) 258488
40.1%
2024-03-30T03:20:19.963282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 612807
13.4%
i 515529
 
11.3%
o 437458
 
9.6%
r 335047
 
7.3%
n 333993
 
7.3%
e 282120
 
6.2%
s 258906
 
5.7%
l 254971
 
5.6%
C 114686
 
2.5%
d 114164
 
2.5%
Other values (37) 1320782
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4580463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 612807
13.4%
i 515529
 
11.3%
o 437458
 
9.6%
r 335047
 
7.3%
n 333993
 
7.3%
e 282120
 
6.2%
s 258906
 
5.7%
l 254971
 
5.6%
C 114686
 
2.5%
d 114164
 
2.5%
Other values (37) 1320782
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4580463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 612807
13.4%
i 515529
 
11.3%
o 437458
 
9.6%
r 335047
 
7.3%
n 333993
 
7.3%
e 282120
 
6.2%
s 258906
 
5.7%
l 254971
 
5.6%
C 114686
 
2.5%
d 114164
 
2.5%
Other values (37) 1320782
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4580463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 612807
13.4%
i 515529
 
11.3%
o 437458
 
9.6%
r 335047
 
7.3%
n 333993
 
7.3%
e 282120
 
6.2%
s 258906
 
5.7%
l 254971
 
5.6%
C 114686
 
2.5%
d 114164
 
2.5%
Other values (37) 1320782
28.8%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.029919
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:20.374280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q381
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.784386
Coefficient of variation (CV)0.49573247
Kurtosis-1.3258284
Mean54.029919
Median Absolute Deviation (MAD)22
Skewness0.0077426799
Sum30334612
Variance717.40331
MonotonicityNot monotonic
2024-03-30T03:20:20.699446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 59719
 
10.6%
74 57175
 
10.2%
33 53897
 
9.6%
22 32235
 
5.7%
34 29883
 
5.3%
41 29179
 
5.2%
82 24955
 
4.4%
36 22859
 
4.1%
38 19351
 
3.4%
85 17332
 
3.1%
Other values (42) 214856
38.3%
ValueCountFrequency (%)
1 2666
 
0.5%
2 10977
2.0%
3 2776
 
0.5%
4 533
 
0.1%
5 98
 
< 0.1%
11 1957
 
0.3%
12 1133
 
0.2%
13 12214
2.2%
14 588
 
0.1%
15 1335
 
0.2%
ValueCountFrequency (%)
93 14912
 
2.7%
92 6261
 
1.1%
91 59719
10.6%
88 624
 
0.1%
87 9353
 
1.7%
86 2123
 
0.4%
85 17332
 
3.1%
84 1772
 
0.3%
83 2253
 
0.4%
82 24955
4.4%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12654.137
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:21.183101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1527.4225
Coefficient of variation (CV)0.12070538
Kurtosis-1.2972653
Mean12654.137
Median Absolute Deviation (MAD)1591
Skewness0.10121465
Sum7.1045514 × 109
Variance2333019.3
MonotonicityNot monotonic
2024-03-30T03:20:21.617190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 27543
 
4.9%
11298 22856
 
4.1%
11292 22733
 
4.0%
13930 21185
 
3.8%
12889 15802
 
2.8%
11057 15672
 
2.8%
12892 15666
 
2.8%
14107 14925
 
2.7%
13204 14083
 
2.5%
12953 13819
 
2.5%
Other values (330) 377157
67.2%
ValueCountFrequency (%)
10135 350
 
0.1%
10136 88
 
< 0.1%
10140 1869
0.3%
10141 60
 
< 0.1%
10146 85
 
< 0.1%
10155 88
 
< 0.1%
10157 120
 
< 0.1%
10158 292
 
0.1%
10165 9
 
< 0.1%
10170 60
 
< 0.1%
ValueCountFrequency (%)
16869 140
 
< 0.1%
16218 120
 
< 0.1%
15991 60
 
< 0.1%
15919 908
0.2%
15841 60
 
< 0.1%
15624 793
0.1%
15607 87
 
< 0.1%
15582 51
 
< 0.1%
15569 51
 
< 0.1%
15412 1050
0.2%

DEST
Text

Distinct340
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:22.497828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1684323
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLGA
2nd rowJFK
3rd rowITH
4th rowJFK
5th rowCLE
ValueCountFrequency (%)
atl 27543
 
4.9%
dfw 22856
 
4.1%
den 22733
 
4.0%
ord 21185
 
3.8%
las 15802
 
2.8%
clt 15672
 
2.8%
lax 15666
 
2.8%
phx 14925
 
2.7%
mco 14083
 
2.5%
lga 13819
 
2.5%
Other values (330) 377157
67.2%
2024-03-30T03:20:23.572069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 190503
 
11.3%
L 155995
 
9.3%
S 144073
 
8.6%
D 130497
 
7.7%
T 88775
 
5.3%
O 86673
 
5.1%
C 84436
 
5.0%
M 75158
 
4.5%
F 70274
 
4.2%
P 66528
 
3.9%
Other values (16) 591411
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 190503
 
11.3%
L 155995
 
9.3%
S 144073
 
8.6%
D 130497
 
7.7%
T 88775
 
5.3%
O 86673
 
5.1%
C 84436
 
5.0%
M 75158
 
4.5%
F 70274
 
4.2%
P 66528
 
3.9%
Other values (16) 591411
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 190503
 
11.3%
L 155995
 
9.3%
S 144073
 
8.6%
D 130497
 
7.7%
T 88775
 
5.3%
O 86673
 
5.1%
C 84436
 
5.0%
M 75158
 
4.5%
F 70274
 
4.2%
P 66528
 
3.9%
Other values (16) 591411
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 190503
 
11.3%
L 155995
 
9.3%
S 144073
 
8.6%
D 130497
 
7.7%
T 88775
 
5.3%
O 86673
 
5.1%
C 84436
 
5.0%
M 75158
 
4.5%
F 70274
 
4.2%
P 66528
 
3.9%
Other values (16) 591411
35.1%
Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:24.197625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.060329
Min length8

Characters and Unicode

Total characters7332604
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowNew York, NY
3rd rowIthaca/Cortland, NY
4th rowNew York, NY
5th rowCleveland, OH
ValueCountFrequency (%)
ca 59729
 
4.6%
tx 57168
 
4.4%
fl 53882
 
4.1%
ny 32237
 
2.5%
ga 29891
 
2.3%
new 29806
 
2.3%
il 29180
 
2.2%
san 28394
 
2.2%
chicago 28015
 
2.1%
atlanta 27543
 
2.1%
Other values (406) 935517
71.3%
2024-03-30T03:20:25.109097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
749921
 
10.2%
a 561840
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387111
 
5.3%
n 356910
 
4.9%
t 348511
 
4.8%
l 320763
 
4.4%
i 276074
 
3.8%
r 268800
 
3.7%
Other values (46) 3097753
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7332604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
749921
 
10.2%
a 561840
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387111
 
5.3%
n 356910
 
4.9%
t 348511
 
4.8%
l 320763
 
4.4%
i 276074
 
3.8%
r 268800
 
3.7%
Other values (46) 3097753
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7332604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
749921
 
10.2%
a 561840
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387111
 
5.3%
n 356910
 
4.9%
t 348511
 
4.8%
l 320763
 
4.4%
i 276074
 
3.8%
r 268800
 
3.7%
Other values (46) 3097753
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7332604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
749921
 
10.2%
a 561840
 
7.7%
, 561441
 
7.7%
o 403480
 
5.5%
e 387111
 
5.3%
n 356910
 
4.9%
t 348511
 
4.8%
l 320763
 
4.4%
i 276074
 
3.8%
r 268800
 
3.7%
Other values (46) 3097753
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:25.538709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1585545
Min length4

Characters and Unicode

Total characters4580547
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNew York
3rd rowNew York
4th rowNew York
5th rowOhio
ValueCountFrequency (%)
california 59729
 
9.3%
texas 57168
 
8.9%
florida 53882
 
8.3%
new 47462
 
7.4%
york 32237
 
5.0%
georgia 29891
 
4.6%
illinois 29180
 
4.5%
carolina 28061
 
4.3%
colorado 24950
 
3.9%
north 24266
 
3.8%
Other values (51) 258490
40.1%
2024-03-30T03:20:26.316965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 612828
13.4%
i 515550
 
11.3%
o 437466
 
9.6%
r 335052
 
7.3%
n 333990
 
7.3%
e 282133
 
6.2%
s 258908
 
5.7%
l 254966
 
5.6%
C 114698
 
2.5%
d 114147
 
2.5%
Other values (37) 1320809
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4580547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 612828
13.4%
i 515550
 
11.3%
o 437466
 
9.6%
r 335052
 
7.3%
n 333990
 
7.3%
e 282133
 
6.2%
s 258908
 
5.7%
l 254966
 
5.6%
C 114698
 
2.5%
d 114147
 
2.5%
Other values (37) 1320809
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4580547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 612828
13.4%
i 515550
 
11.3%
o 437466
 
9.6%
r 335052
 
7.3%
n 333990
 
7.3%
e 282133
 
6.2%
s 258908
 
5.7%
l 254966
 
5.6%
C 114698
 
2.5%
d 114147
 
2.5%
Other values (37) 1320809
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4580547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 612828
13.4%
i 515550
 
11.3%
o 437466
 
9.6%
r 335052
 
7.3%
n 333990
 
7.3%
e 282133
 
6.2%
s 258908
 
5.7%
l 254966
 
5.6%
C 114698
 
2.5%
d 114147
 
2.5%
Other values (37) 1320809
28.8%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.030856
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:26.675369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q133
median44
Q381
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.784294
Coefficient of variation (CV)0.49572217
Kurtosis-1.3258143
Mean54.030856
Median Absolute Deviation (MAD)22
Skewness0.0077027066
Sum30335138
Variance717.39838
MonotonicityNot monotonic
2024-03-30T03:20:27.043445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 59729
 
10.6%
74 57168
 
10.2%
33 53882
 
9.6%
22 32237
 
5.7%
34 29891
 
5.3%
41 29180
 
5.2%
82 24950
 
4.4%
36 22861
 
4.1%
38 19351
 
3.4%
85 17332
 
3.1%
Other values (42) 214860
38.3%
ValueCountFrequency (%)
1 2667
 
0.5%
2 10977
2.0%
3 2771
 
0.5%
4 534
 
0.1%
5 98
 
< 0.1%
11 1958
 
0.3%
12 1135
 
0.2%
13 12214
2.2%
14 589
 
0.1%
15 1336
 
0.2%
ValueCountFrequency (%)
93 14910
 
2.7%
92 6264
 
1.1%
91 59729
10.6%
88 623
 
0.1%
87 9350
 
1.7%
86 2128
 
0.4%
85 17332
 
3.1%
84 1772
 
0.3%
83 2251
 
0.4%
82 24950
4.4%

CRS_DEP_TIME
Real number (ℝ)

Distinct1202
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1334.9524
Minimum3
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:27.428290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile600
Q1908
median1325
Q31745
95-th percentile2135
Maximum2359
Range2356
Interquartile range (IQR)837

Descriptive statistics

Standard deviation499.16463
Coefficient of variation (CV)0.37391941
Kurtosis-1.0880355
Mean1334.9524
Median Absolute Deviation (MAD)420
Skewness0.079931655
Sum7.4949703 × 108
Variance249165.33
MonotonicityNot monotonic
2024-03-30T03:20:28.180878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12315
 
2.2%
700 8818
 
1.6%
800 5486
 
1.0%
900 3597
 
0.6%
630 3399
 
0.6%
830 3285
 
0.6%
1000 3152
 
0.6%
615 3102
 
0.6%
730 3069
 
0.5%
1100 2953
 
0.5%
Other values (1192) 512265
91.2%
ValueCountFrequency (%)
3 3
 
< 0.1%
4 1
 
< 0.1%
5 13
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 10
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 13
< 0.1%
ValueCountFrequency (%)
2359 865
0.2%
2358 1
 
< 0.1%
2357 59
 
< 0.1%
2356 26
 
< 0.1%
2355 215
 
< 0.1%
2354 61
 
< 0.1%
2353 58
 
< 0.1%
2351 1
 
< 0.1%
2350 120
 
< 0.1%
2349 56
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1408
Distinct (%)0.3%
Missing9310
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean1338.5263
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:28.796975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile557
Q1909
median1328
Q31754
95-th percentile2153
Maximum2400
Range2399
Interquartile range (IQR)845

Descriptive statistics

Standard deviation516.31454
Coefficient of variation (CV)0.38573357
Kurtosis-1.0017798
Mean1338.5263
Median Absolute Deviation (MAD)423
Skewness0.028552518
Sum7.3904188 × 108
Variance266580.7
MonotonicityNot monotonic
2024-03-30T03:20:29.255787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1535
 
0.3%
556 1359
 
0.2%
557 1348
 
0.2%
554 1302
 
0.2%
559 1211
 
0.2%
558 1209
 
0.2%
655 1151
 
0.2%
553 1101
 
0.2%
600 1094
 
0.2%
654 1039
 
0.2%
Other values (1398) 539782
96.1%
(Missing) 9310
 
1.7%
ValueCountFrequency (%)
1 88
< 0.1%
2 78
< 0.1%
3 61
< 0.1%
4 61
< 0.1%
5 71
< 0.1%
6 61
< 0.1%
7 52
< 0.1%
8 61
< 0.1%
9 65
< 0.1%
10 33
 
< 0.1%
ValueCountFrequency (%)
2400 65
< 0.1%
2359 98
< 0.1%
2358 91
< 0.1%
2357 115
< 0.1%
2356 108
< 0.1%
2355 120
< 0.1%
2354 98
< 0.1%
2353 125
< 0.1%
2352 124
< 0.1%
2351 110
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1130
Distinct (%)0.2%
Missing9311
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean13.723784
Minimum-68
Maximum2935
Zeros25076
Zeros (%)4.5%
Negative305859
Negative (%)54.5%
Memory size4.3 MiB
2024-03-30T03:20:29.573610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-68
5-th percentile-10
Q1-5
median-2
Q311
95-th percentile85
Maximum2935
Range3003
Interquartile range (IQR)16

Descriptive statistics

Standard deviation56.065508
Coefficient of variation (CV)4.0852805
Kurtosis220.67919
Mean13.723784
Median Absolute Deviation (MAD)5
Skewness11.047413
Sum7577313
Variance3143.3412
MonotonicityNot monotonic
2024-03-30T03:20:29.911809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 40116
 
7.1%
-4 37252
 
6.6%
-3 36220
 
6.5%
-2 33031
 
5.9%
-6 32152
 
5.7%
-1 29644
 
5.3%
-7 26682
 
4.8%
0 25076
 
4.5%
-8 21487
 
3.8%
-9 15742
 
2.8%
Other values (1120) 254728
45.4%
ValueCountFrequency (%)
-68 1
 
< 0.1%
-52 1
 
< 0.1%
-47 2
< 0.1%
-45 1
 
< 0.1%
-43 1
 
< 0.1%
-40 3
< 0.1%
-37 1
 
< 0.1%
-36 2
< 0.1%
-35 4
< 0.1%
-34 1
 
< 0.1%
ValueCountFrequency (%)
2935 1
< 0.1%
2668 1
< 0.1%
2665 1
< 0.1%
2383 1
< 0.1%
2327 1
< 0.1%
2281 1
< 0.1%
2275 1
< 0.1%
1865 1
< 0.1%
1850 1
< 0.1%
1793 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct164
Distinct (%)< 0.1%
Missing9528
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean17.275572
Minimum1
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:30.508488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile33
Maximum196
Range195
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.2430334
Coefficient of variation (CV)0.53503486
Kurtosis24.22288
Mean17.275572
Median Absolute Deviation (MAD)4
Skewness3.5153901
Sum9534613
Variance85.433666
MonotonicityNot monotonic
2024-03-30T03:20:30.954368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 45075
 
8.0%
13 44920
 
8.0%
11 42334
 
7.5%
14 42058
 
7.5%
15 38205
 
6.8%
10 34416
 
6.1%
16 33611
 
6.0%
17 29133
 
5.2%
18 25464
 
4.5%
9 24606
 
4.4%
Other values (154) 192091
34.2%
ValueCountFrequency (%)
1 11
 
< 0.1%
2 20
 
< 0.1%
3 68
 
< 0.1%
4 222
 
< 0.1%
5 620
 
0.1%
6 2339
 
0.4%
7 6969
 
1.2%
8 14440
2.6%
9 24606
4.4%
10 34416
6.1%
ValueCountFrequency (%)
196 1
< 0.1%
179 1
< 0.1%
172 1
< 0.1%
171 2
< 0.1%
166 1
< 0.1%
165 1
< 0.1%
163 1
< 0.1%
161 1
< 0.1%
160 2
< 0.1%
159 1
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct147
Distinct (%)< 0.1%
Missing9769
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean7.9866243
Minimum1
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:31.385809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum194
Range193
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.3066904
Coefficient of variation (CV)0.78965658
Kurtosis46.043104
Mean7.9866243
Median Absolute Deviation (MAD)2
Skewness4.6103627
Sum4405997
Variance39.774344
MonotonicityNot monotonic
2024-03-30T03:20:31.844123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 82074
14.6%
5 78586
14.0%
6 65628
11.7%
7 53765
9.6%
3 49743
8.9%
8 41669
7.4%
9 32488
 
5.8%
10 25641
 
4.6%
11 19811
 
3.5%
12 15668
 
2.8%
Other values (137) 86599
15.4%
ValueCountFrequency (%)
1 777
 
0.1%
2 12400
 
2.2%
3 49743
8.9%
4 82074
14.6%
5 78586
14.0%
6 65628
11.7%
7 53765
9.6%
8 41669
7.4%
9 32488
 
5.8%
10 25641
 
4.6%
ValueCountFrequency (%)
194 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 1
< 0.1%
173 1
< 0.1%
169 1
< 0.1%
167 1
< 0.1%
166 2
< 0.1%
160 2
< 0.1%
157 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1487.1325
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:32.280620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile715
Q11057
median1515
Q31929
95-th percentile2302
Maximum2359
Range2358
Interquartile range (IQR)872

Descriptive statistics

Standard deviation529.72473
Coefficient of variation (CV)0.35620546
Kurtosis-0.49208117
Mean1487.1325
Median Absolute Deviation (MAD)415
Skewness-0.29452539
Sum8.3493718 × 108
Variance280608.29
MonotonicityNot monotonic
2024-03-30T03:20:32.831515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3351
 
0.6%
2100 1802
 
0.3%
1810 1666
 
0.3%
900 1632
 
0.3%
2150 1610
 
0.3%
2200 1609
 
0.3%
1400 1608
 
0.3%
1940 1600
 
0.3%
950 1599
 
0.3%
1915 1590
 
0.3%
Other values (1295) 543374
96.8%
ValueCountFrequency (%)
1 51
 
< 0.1%
2 28
 
< 0.1%
3 54
 
< 0.1%
4 98
 
< 0.1%
5 760
0.1%
6 87
 
< 0.1%
7 78
 
< 0.1%
8 41
 
< 0.1%
9 103
 
< 0.1%
10 548
0.1%
ValueCountFrequency (%)
2359 3351
0.6%
2358 628
 
0.1%
2357 828
 
0.1%
2356 758
 
0.1%
2355 1035
 
0.2%
2354 463
 
0.1%
2353 378
 
0.1%
2352 387
 
0.1%
2351 428
 
0.1%
2350 971
 
0.2%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.3%
Missing9769
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean1450.6796
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:33.219447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile608
Q11035
median1458
Q31918
95-th percentile2256
Maximum2400
Range2399
Interquartile range (IQR)883

Descriptive statistics

Standard deviation559.38211
Coefficient of variation (CV)0.38560004
Kurtosis-0.37467569
Mean1450.6796
Median Absolute Deviation (MAD)442
Skewness-0.39282352
Sum8.002993 × 108
Variance312908.34
MonotonicityNot monotonic
2024-03-30T03:20:34.601244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1847 642
 
0.1%
2143 625
 
0.1%
1634 620
 
0.1%
1637 610
 
0.1%
1152 606
 
0.1%
1642 604
 
0.1%
908 599
 
0.1%
1841 599
 
0.1%
1640 597
 
0.1%
1621 597
 
0.1%
Other values (1430) 545573
97.2%
(Missing) 9769
 
1.7%
ValueCountFrequency (%)
1 395
0.1%
2 340
0.1%
3 325
0.1%
4 332
0.1%
5 345
0.1%
6 315
0.1%
7 330
0.1%
8 287
0.1%
9 341
0.1%
10 306
0.1%
ValueCountFrequency (%)
2400 331
0.1%
2359 394
0.1%
2358 374
0.1%
2357 408
0.1%
2356 372
0.1%
2355 443
0.1%
2354 449
0.1%
2353 438
0.1%
2352 440
0.1%
2351 459
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1181
Distinct (%)0.2%
Missing11192
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean9.1129598
Minimum-94
Maximum2916
Zeros10477
Zeros (%)1.9%
Negative320273
Negative (%)57.0%
Memory size4.3 MiB
2024-03-30T03:20:35.123226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-94
5-th percentile-26
Q1-14
median-4
Q312
95-th percentile86
Maximum2916
Range3010
Interquartile range (IQR)26

Descriptive statistics

Standard deviation58.170159
Coefficient of variation (CV)6.3832345
Kurtosis191.68988
Mean9.1129598
Median Absolute Deviation (MAD)12
Skewness10.021578
Sum5014397
Variance3383.7674
MonotonicityNot monotonic
2024-03-30T03:20:35.513324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 15345
 
2.7%
-9 15012
 
2.7%
-11 14963
 
2.7%
-8 14927
 
2.7%
-12 14901
 
2.7%
-7 14600
 
2.6%
-13 14295
 
2.5%
-6 14137
 
2.5%
-14 13886
 
2.5%
-5 13701
 
2.4%
Other values (1171) 404482
72.0%
ValueCountFrequency (%)
-94 1
 
< 0.1%
-92 1
 
< 0.1%
-89 1
 
< 0.1%
-85 1
 
< 0.1%
-84 1
 
< 0.1%
-83 1
 
< 0.1%
-81 2
< 0.1%
-80 1
 
< 0.1%
-79 2
< 0.1%
-78 4
< 0.1%
ValueCountFrequency (%)
2916 1
< 0.1%
2656 1
< 0.1%
2655 1
< 0.1%
2382 1
< 0.1%
2314 1
< 0.1%
2270 1
< 0.1%
2258 1
< 0.1%
1864 1
< 0.1%
1847 1
< 0.1%
1797 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0.0
551852 
1.0
 
9589

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1684323
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 551852
98.3%
1.0 9589
 
1.7%

Length

2024-03-30T03:20:35.832535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:20:36.085732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 551852
98.3%
1.0 9589
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1113293
66.1%
. 561441
33.3%
1 9589
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1113293
66.1%
. 561441
33.3%
1 9589
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1113293
66.1%
. 561441
33.3%
1 9589
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1113293
66.1%
. 561441
33.3%
1 9589
 
0.6%

CANCELLATION_CODE
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing551852
Missing (%)98.3%
Memory size4.3 MiB
B
5165 
A
2962 
C
1461 
D
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9589
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 5165
 
0.9%
A 2962
 
0.5%
C 1461
 
0.3%
D 1
 
< 0.1%
(Missing) 551852
98.3%

Length

2024-03-30T03:20:36.323796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:20:36.547272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 5165
53.9%
a 2962
30.9%
c 1461
 
15.2%
d 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 5165
53.9%
A 2962
30.9%
C 1461
 
15.2%
D 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 5165
53.9%
A 2962
30.9%
C 1461
 
15.2%
D 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 5165
53.9%
A 2962
30.9%
C 1461
 
15.2%
D 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 5165
53.9%
A 2962
30.9%
C 1461
 
15.2%
D 1
 
< 0.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0.0
559838 
1.0
 
1603

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1684323
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 559838
99.7%
1.0 1603
 
0.3%

Length

2024-03-30T03:20:36.804209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:20:37.022394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 559838
99.7%
1.0 1603
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1121279
66.6%
. 561441
33.3%
1 1603
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1121279
66.6%
. 561441
33.3%
1 1603
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1121279
66.6%
. 561441
33.3%
1 1603
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1684323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1121279
66.6%
. 561441
33.3%
1 1603
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct623
Distinct (%)0.1%
Missing11192
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean114.88136
Minimum8
Maximum686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:37.423010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q163
median98
Q3145
95-th percentile272
Maximum686
Range678
Interquartile range (IQR)82

Descriptive statistics

Standard deviation70.591469
Coefficient of variation (CV)0.61447281
Kurtosis2.5124361
Mean114.88136
Median Absolute Deviation (MAD)39
Skewness1.4161643
Sum63213351
Variance4983.1555
MonotonicityNot monotonic
2024-03-30T03:20:37.779214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 4642
 
0.8%
64 4634
 
0.8%
61 4607
 
0.8%
58 4598
 
0.8%
66 4595
 
0.8%
65 4534
 
0.8%
56 4508
 
0.8%
57 4501
 
0.8%
63 4443
 
0.8%
54 4437
 
0.8%
Other values (613) 504750
89.9%
(Missing) 11192
 
2.0%
ValueCountFrequency (%)
8 5
 
< 0.1%
9 18
 
< 0.1%
10 16
 
< 0.1%
11 5
 
< 0.1%
12 4
 
< 0.1%
13 8
 
< 0.1%
14 9
 
< 0.1%
15 37
 
< 0.1%
16 91
< 0.1%
17 142
< 0.1%
ValueCountFrequency (%)
686 1
 
< 0.1%
673 1
 
< 0.1%
669 1
 
< 0.1%
666 1
 
< 0.1%
663 1
 
< 0.1%
658 1
 
< 0.1%
657 3
< 0.1%
654 1
 
< 0.1%
653 1
 
< 0.1%
651 1
 
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct892
Distinct (%)0.7%
Missing435559
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean23.981673
Minimum0
Maximum2916
Zeros54172
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:38.159632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q322
95-th percentile97
Maximum2916
Range2916
Interquartile range (IQR)22

Descriptive statistics

Standard deviation73.423339
Coefficient of variation (CV)3.0616437
Kurtosis182.0958
Mean23.981673
Median Absolute Deviation (MAD)4
Skewness10.800042
Sum3018861
Variance5390.9867
MonotonicityNot monotonic
2024-03-30T03:20:38.526392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54172
 
9.6%
1 2382
 
0.4%
3 2366
 
0.4%
6 2334
 
0.4%
2 2321
 
0.4%
4 2230
 
0.4%
7 2183
 
0.4%
15 2154
 
0.4%
5 2147
 
0.4%
8 1975
 
0.4%
Other values (882) 51618
 
9.2%
(Missing) 435559
77.6%
ValueCountFrequency (%)
0 54172
9.6%
1 2382
 
0.4%
2 2321
 
0.4%
3 2366
 
0.4%
4 2230
 
0.4%
5 2147
 
0.4%
6 2334
 
0.4%
7 2183
 
0.4%
8 1975
 
0.4%
9 1963
 
0.3%
ValueCountFrequency (%)
2916 1
< 0.1%
2656 1
< 0.1%
2655 1
< 0.1%
2328 1
< 0.1%
1864 1
< 0.1%
1789 1
< 0.1%
1787 1
< 0.1%
1759 1
< 0.1%
1748 1
< 0.1%
1737 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct435
Distinct (%)0.3%
Missing435559
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean3.5527716
Minimum0
Maximum1643
Zeros119465
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:38.876568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum1643
Range1643
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.089189
Coefficient of variation (CV)8.7506859
Kurtosis727.16363
Mean3.5527716
Median Absolute Deviation (MAD)0
Skewness22.399204
Sum447230
Variance966.53767
MonotonicityNot monotonic
2024-03-30T03:20:39.268632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 119465
 
21.3%
7 137
 
< 0.1%
15 134
 
< 0.1%
6 128
 
< 0.1%
17 126
 
< 0.1%
2 124
 
< 0.1%
16 116
 
< 0.1%
18 116
 
< 0.1%
3 115
 
< 0.1%
1 112
 
< 0.1%
Other values (425) 5309
 
0.9%
(Missing) 435559
77.6%
ValueCountFrequency (%)
0 119465
21.3%
1 112
 
< 0.1%
2 124
 
< 0.1%
3 115
 
< 0.1%
4 103
 
< 0.1%
5 110
 
< 0.1%
6 128
 
< 0.1%
7 137
 
< 0.1%
8 92
 
< 0.1%
9 98
 
< 0.1%
ValueCountFrequency (%)
1643 1
< 0.1%
1609 1
< 0.1%
1503 1
< 0.1%
1446 1
< 0.1%
1361 1
< 0.1%
1337 1
< 0.1%
1270 1
< 0.1%
1256 1
< 0.1%
1204 1
< 0.1%
1188 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct452
Distinct (%)0.4%
Missing435559
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean13.751386
Minimum0
Maximum1660
Zeros64125
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:39.620960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile58
Maximum1660
Range1660
Interquartile range (IQR)16

Descriptive statistics

Standard deviation36.254634
Coefficient of variation (CV)2.6364349
Kurtosis314.55699
Mean13.751386
Median Absolute Deviation (MAD)0
Skewness12.352336
Sum1731052
Variance1314.3985
MonotonicityNot monotonic
2024-03-30T03:20:40.112440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64125
 
11.4%
1 3407
 
0.6%
2 2510
 
0.4%
3 2344
 
0.4%
15 2344
 
0.4%
4 2196
 
0.4%
16 2040
 
0.4%
5 2032
 
0.4%
6 1957
 
0.3%
17 1902
 
0.3%
Other values (442) 41025
 
7.3%
(Missing) 435559
77.6%
ValueCountFrequency (%)
0 64125
11.4%
1 3407
 
0.6%
2 2510
 
0.4%
3 2344
 
0.4%
4 2196
 
0.4%
5 2032
 
0.4%
6 1957
 
0.3%
7 1781
 
0.3%
8 1741
 
0.3%
9 1566
 
0.3%
ValueCountFrequency (%)
1660 1
< 0.1%
1487 1
< 0.1%
1409 2
< 0.1%
1402 1
< 0.1%
1401 1
< 0.1%
1358 1
< 0.1%
1300 1
< 0.1%
1276 1
< 0.1%
1191 1
< 0.1%
1178 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct102
Distinct (%)0.1%
Missing435559
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean0.14313405
Minimum0
Maximum368
Zeros125147
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:40.537897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum368
Range368
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0125307
Coefficient of variation (CV)21.046919
Kurtosis4090.4577
Mean0.14313405
Median Absolute Deviation (MAD)0
Skewness50.383342
Sum18018
Variance9.0753411
MonotonicityNot monotonic
2024-03-30T03:20:40.933879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125147
 
22.3%
9 34
 
< 0.1%
16 33
 
< 0.1%
15 28
 
< 0.1%
17 27
 
< 0.1%
10 27
 
< 0.1%
6 27
 
< 0.1%
8 26
 
< 0.1%
18 23
 
< 0.1%
14 22
 
< 0.1%
Other values (92) 488
 
0.1%
(Missing) 435559
77.6%
ValueCountFrequency (%)
0 125147
22.3%
1 10
 
< 0.1%
2 17
 
< 0.1%
3 21
 
< 0.1%
4 22
 
< 0.1%
5 20
 
< 0.1%
6 27
 
< 0.1%
7 19
 
< 0.1%
8 26
 
< 0.1%
9 34
 
< 0.1%
ValueCountFrequency (%)
368 1
< 0.1%
332 1
< 0.1%
228 1
< 0.1%
191 1
< 0.1%
190 1
< 0.1%
184 1
< 0.1%
152 1
< 0.1%
149 1
< 0.1%
136 2
< 0.1%
135 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct642
Distinct (%)0.5%
Missing435559
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean27.631504
Minimum0
Maximum2258
Zeros59767
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2024-03-30T03:20:41.290294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333
95-th percentile121
Maximum2258
Range2258
Interquartile range (IQR)33

Descriptive statistics

Standard deviation58.881248
Coefficient of variation (CV)2.1309462
Kurtosis120.76667
Mean27.631504
Median Absolute Deviation (MAD)4
Skewness7.4997916
Sum3478309
Variance3467.0014
MonotonicityNot monotonic
2024-03-30T03:20:41.681596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59767
 
10.6%
15 1611
 
0.3%
16 1469
 
0.3%
17 1408
 
0.3%
19 1378
 
0.2%
18 1374
 
0.2%
21 1275
 
0.2%
20 1252
 
0.2%
14 1202
 
0.2%
13 1190
 
0.2%
Other values (632) 53956
 
9.6%
(Missing) 435559
77.6%
ValueCountFrequency (%)
0 59767
10.6%
1 819
 
0.1%
2 907
 
0.2%
3 905
 
0.2%
4 865
 
0.2%
5 917
 
0.2%
6 984
 
0.2%
7 986
 
0.2%
8 1069
 
0.2%
9 1047
 
0.2%
ValueCountFrequency (%)
2258 1
< 0.1%
2216 1
< 0.1%
1905 1
< 0.1%
1553 1
< 0.1%
1522 1
< 0.1%
1507 1
< 0.1%
1407 1
< 0.1%
1401 1
< 0.1%
1332 1
< 0.1%
1298 1
< 0.1%

Interactions

2024-03-30T03:19:50.837284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:35.763359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:48.505145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:00.012944image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:10.345054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:20.968035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:29.726377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:36.246903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:42.297612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:48.167491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:56.455110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:11.454753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:34.563485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:48.687001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:02.236939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:11.156912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:24.627279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:31.655293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:37.293060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:44.643519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:51.234476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:36.494527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:48.910911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:00.659287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:10.945975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:21.482042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:30.088922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:36.729678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:42.586399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:48.467794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:57.780786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:13.956681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:34.907219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:49.290821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:02.864606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:11.629659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:24.927221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:31.958823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:37.554461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:44.958344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:51.489456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:36.872089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:49.277038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:01.412737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:11.357440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:22.140114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:30.407572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:37.044038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:42.873280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:48.743218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:59.605947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:14.942112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:35.212477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:49.871803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:03.410052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:12.366336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:25.256807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:32.268098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:37.806175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:45.249758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:51.779344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:37.723485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:49.659574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:01.933993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:11.758259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:22.530741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:30.717785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:37.389648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:43.205711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:49.051452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:00.282546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:15.961294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:35.585770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:50.606441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:03.869805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:13.430441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:25.541387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:32.546119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:38.106110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:45.618216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:52.079406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:38.209426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:50.041731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:02.451181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:12.271362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:22.881364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:30.995770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:37.718706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:43.491192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:49.325406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:00.928774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:16.850413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:36.287194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:51.155377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:04.278195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:15.457933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:25.812804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:32.954191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:38.371177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:45.893834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:52.386611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:38.633367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:50.471126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:02.953354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:12.787614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:23.261431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:31.297661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:38.040622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:43.814409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:49.635194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:01.483455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:18.164262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:37.167910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:51.828912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:04.702357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:16.418363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:26.165310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:33.221360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:38.630203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:46.300825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:52.624487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:39.587786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:50.805169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:03.372545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:13.219251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:23.636725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:31.589738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:38.335279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:44.149346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:49.923271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:01.913587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:18:06.922379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:32.867793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:45.858198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:59.582160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:09.866712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:23.281003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:30.303760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:36.253782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:43.329482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:49.553001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:55.858562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:47.377240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:58.861276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:09.143847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:19.299506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:28.523530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:35.307252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:41.468622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:47.354271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:54.818158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:07.813671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:33.168714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:46.631612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:00.304570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:10.206537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:23.633683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:30.675740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:36.504760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:43.735104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:49.850934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:56.194713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:47.666004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:59.197645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:09.499961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:19.704040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:28.794803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:35.577600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:41.690511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:47.589155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:55.334018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:08.989344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:33.472164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:47.317136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:00.869052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:10.501217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:23.938463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:31.011049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:36.756233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:44.054203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:50.190179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:56.505526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:47.966697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:16:59.534422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:09.854538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:20.178411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:29.199796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:35.851520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:41.942787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:47.824715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:17:55.789044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:09.919811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:33.986442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:18:47.934225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:01.641841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:10.792307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:24.303077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:31.351803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:37.053043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:44.397589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:19:50.530792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T03:19:57.381236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:20:01.984967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
014/3/2023 12:00:00 AM9E462814576ROCRochester, NYNew York2212953LGANew York, NYNew York22937932.0-5.07.05.010591031.0-28.00.0NaN0.047.0NaNNaNNaNNaNNaN
114/3/2023 12:00:00 AM9E462912397ITHIthaca/Cortland, NYNew York2212478JFKNew York, NYNew York2214431438.0-5.012.07.016091540.0-29.00.0NaN0.043.0NaNNaNNaNNaNNaN
214/3/2023 12:00:00 AM9E462912478JFKNew York, NYNew York2212397ITHIthaca/Cortland, NYNew York2212501249.0-1.025.02.013581400.02.00.0NaN0.044.0NaNNaNNaNNaNNaN
314/3/2023 12:00:00 AM9E463011042CLECleveland, OHOhio4412478JFKNew York, NYNew York2217001656.0-4.026.09.018371838.01.00.0NaN0.067.0NaNNaNNaNNaNNaN
414/3/2023 12:00:00 AM9E463212478JFKNew York, NYNew York2211042CLECleveland, OHOhio4414591455.0-4.025.07.016561646.0-10.00.0NaN0.079.0NaNNaNNaNNaNNaN
514/3/2023 12:00:00 AM9E463311042CLECleveland, OHOhio4412953LGANew York, NYNew York2217591754.0-5.011.04.019361924.0-12.00.0NaN0.075.0NaNNaNNaNNaNNaN
614/3/2023 12:00:00 AM9E463412953LGANew York, NYNew York2211042CLECleveland, OHOhio4412551251.0-4.028.08.014521440.0-12.00.0NaN0.073.0NaNNaNNaNNaNNaN
714/3/2023 12:00:00 AM9E463510821BWIBaltimore, MDMaryland3512478JFKNew York, NYNew York2217151801.046.021.015.018371920.043.00.0NaN0.043.00.00.00.00.043.0
814/3/2023 12:00:00 AM9E463512478JFKNew York, NYNew York2210821BWIBaltimore, MDMaryland3515091543.034.056.05.016251722.057.00.0NaN0.038.00.00.023.00.034.0
914/3/2023 12:00:00 AM9E463812953LGANew York, NYNew York2213244MEMMemphis, TNTennessee54859855.0-4.019.03.011091044.0-25.00.0NaN0.0147.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
56143174/30/2023 12:00:00 AMYX584910821BWIBaltimore, MDMaryland3510721BOSBoston, MAMassachusetts1319562135.099.017.010.021312301.090.00.0NaN0.059.00.00.00.00.090.0
56143274/30/2023 12:00:00 AMYX585110721BOSBoston, MAMassachusetts1312339INDIndianapolis, INIndiana42822819.0-3.016.06.011101053.0-17.00.0NaN0.0132.0NaNNaNNaNNaNNaN
56143374/30/2023 12:00:00 AMYX585210721BOSBoston, MAMassachusetts1314100PHLPhiladelphia, PAPennsylvania2319552111.076.015.04.021352239.064.00.0NaN0.069.00.00.00.00.064.0
56143474/30/2023 12:00:00 AMYX585312478JFKNew York, NYNew York2210721BOSBoston, MAMassachusetts13600553.0-7.033.017.0715717.02.00.0NaN0.034.0NaNNaNNaNNaNNaN
56143574/30/2023 12:00:00 AMYX585412953LGANew York, NYNew York2210693BNANashville, TNTennessee5410291022.0-7.018.06.012171157.0-20.00.0NaN0.0131.0NaNNaNNaNNaNNaN
56143674/30/2023 12:00:00 AMYX585510821BWIBaltimore, MDMaryland3510721BOSBoston, MAMassachusetts1312151243.028.021.07.013461409.023.00.0NaN0.058.00.00.023.00.00.0
56143774/30/2023 12:00:00 AMYX585611278DCAWashington, DCVirginia3812478JFKNew York, NYNew York2211551222.027.040.08.013231354.031.00.0NaN0.044.00.00.031.00.00.0
56143874/30/2023 12:00:00 AMYX585714524RICRichmond, VAVirginia3812953LGANew York, NYNew York22845937.052.010.04.010071043.036.00.0NaN0.052.00.00.036.00.00.0
56143974/30/2023 12:00:00 AMYX585810721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3812201211.0-9.021.02.014041354.0-10.00.0NaN0.080.0NaNNaNNaNNaNNaN
56144074/30/2023 12:00:00 AMYX586110721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3810151009.0-6.013.07.012001142.0-18.00.0NaN0.073.0NaNNaNNaNNaNNaN